1. Field of the Invention
The present invention relates to model-based pricing systems, and more particularly, to a computer-implemented method for tuning a consumer demand model that produces stable results with respect to random fluctuations of sales data.
2. Description of Related Art
Present pricing methods, while taking into account a tremendous amount of information (such as data collected from point-of-sale systems), ultimately depend upon a pricing analyst's expert intuition for setting prices. This reliance on a human entails a slow and qualitative pricing process. Even if this intuitive process could be captured by an expert system, it would still lack the quantitative description necessary to actually optimize prices. Retailers are being faced with an increasingly competitive and sophisticated environment, leading to lower margins and a stronger focus on retail pricing. Thus, the need for a more rigorous approach to pricing is becoming more urgent.
As known in the art, model-based approaches (e.g., consumer demand modeling) provide a more quantitative approach to pricing than the simple rules-based approaches, such as those embodied in expert systems. However, retailers have found these model-based approaches to be undesirable because they often produce results that are inaccurate and counter-intuitive. For example, one major obstacle to properly modeling consumer demand is that retail sales display a relatively strong stochastic component. Generally, if the price of an item is increased significantly, its sales will drop. However, when an increase in price is relatively small, the corresponding change in sales becomes unpredictable, and may even increase. These kinds of fluctuations tend to wildly mislead a consumer demand model, and may even lead to the conclusion that, as the price is increased, sales will also increase. This is an important problem, considering that only ten to twenty percent of a retailer's price changes are large enough to go beyond the noise present in the sales history and actually probe the demand structure.
Thus, a critical need exists for an efficient approach to pricing that yields accurate results.